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arxiv: 2604.10892 · v2 · submitted 2026-04-13 · 💻 cs.RO · cs.MA

HECTOR: Human-centric Hierarchical Coordination and Supervision of Robotic Fleets under Continual Temporal Tasks

Shen Wang , Yinhang Luo , Jie Li , Meng Guo This is my paper

Pith reviewed 2026-05-15 07:36 UTC · model grok-4.3

classification 💻 cs.RO cs.MA
keywords robotic fleetshuman-robot interactiontemporal taskshierarchical coordinationmulti-robot systemscontinual planningsupervision protocolstemporal logic
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The pith

A three-layer hierarchy lets one operator supervise large robotic fleets on ongoing uncertain tasks with targeted interactions.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces HECTOR, a coordination scheme that organizes human oversight of robot fleets completing continual tasks such as collaborative delivery or search in changing settings. It structures the system into three layers: a protocol for direct multimodal human-fleet communication, periodic reassignment of known tasks to robot teams over a planning window, and real-time coordination inside each team once subtasks emerge during execution. This setup targets missions expressible as temporal logic formulas over joint robot actions. The design aims to cut computational demands and operator workload by matching interaction depth to the needed granularity rather than requiring constant full control.

Core claim

The central claim is that a hierarchical human-centric scheme consisting of bidirectional online interaction, rolling team-level task assignment, and dynamic intra-team coordination enables efficient supervision of large heterogeneous robot fleets under continual temporal tasks and environmental uncertainty, as shown by human-in-the-loop simulations.

What carries the argument

The three-layer hierarchical structure that separates human interaction by granularity and triggering conditions.

If this is right

  • Missions specified as temporal logic over collaborative actions can be handled without enumerating every robot action at the top level.
  • Task reassignments occur only within rolling horizons, limiting replanning to currently known information.
  • Intra-team adjustments respond to detected subtasks without requiring operator input on every local change.
  • Overall fleet performance scales to larger sizes because most coordination decisions stay local or periodic rather than global and continuous.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same layering idea could transfer to fleets of autonomous vehicles where a dispatcher intervenes only at route or priority changes.
  • Real hardware tests would need to measure communication delays between layers when humans respond to alerts.
  • The approach might integrate with existing temporal logic solvers by feeding high-level assignments downward and surfacing only unresolved subtasks upward.

Load-bearing premise

Separating supervision into layers at different scales actually lowers total computation and human interventions while still achieving reliable task completion under uncertainty.

What would settle it

Run identical large-fleet simulations of the same temporal tasks with injected sensor noise, once using the full three-layer HECTOR and once using a single centralized planner with continuous human access, then compare total operator actions required and fraction of missions completed on time.

Figures

Figures reproduced from arXiv: 2604.10892 by Jie Li, Meng Guo, Shen Wang, Yinhang Luo.

Figure 1
Figure 1. Figure 1: The considered scenario where a human operator coordinates, [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: The proposed human-centric framework for hierarchical coordination and supervision of robotic fleets, consisting of three main components: (I) the interaction protocol and interface for four types of online requests (left); (II) the automata-guided task assignment and team formation (middle); (III) three types of local coordination strategy for different tasks (right). Human requests, environment state and… view at source ↗
Figure 4
Figure 4. Figure 4: Integrated interface for human-fleet interaction, which includes [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Illustration of the automaton-guided search tree, where the node expands over not only the local plans of each team tΓk, k P Ku, but also the progress of each mission tQpm, m P Mu. The leaf nodes of complete assignments may correspond to different number of teams. The search structure is organized as a tree T fi pV, Ñq, where V fi tνu is the set of nodes, and ÑĂ V ˆV defines the edges. Each node ν fi ptΓk,… view at source ↗
Figure 6
Figure 6. Figure 6: Illustration of the capacity-based and redundancy-aware team forma￾tion. Given the task assignment ν ‹ K‹ , the team formation tN1, ¨ ¨ ¨ , NK‹ u are the determined optimally under the redundancy margin in (18). proach can be summarized in two key components. (I) Capacity constraints. Binary decision variables bik P t0, 1u are introduced to indicate whether robot i P N is assigned to team Ck. For each acti… view at source ↗
Figure 7
Figure 7. Figure 7: Local coordination results for the static and known local tasks described in Sec. V-C1. In total 14 delivery subtasks (filled circles) are assigned to 5 robots (filled pentagons), of which the trajectories are shown for fully-actuated (left) and non-holonomic (right) robots. collective execution time for task ω ℓ k , while simultaneously optimizing trajectories and task assignments. ■ Uncertainty in the nu… view at source ↗
Figure 9
Figure 9. Figure 9: Execution results of the dynamic and known subtasks in Sec. V-C3. Top: the trajectories of 5 robots (filled hexagons) and 5 targets (filled circles) at different snapshots; Bottom: the evolution of assigned coalitions for each target. Note that different colors reflect different capacity constraints. i.e., τ iptq “ pti , pi , aiq for the currently assigned subtask, and its trajectory xiptq follows the coal… view at source ↗
Figure 10
Figure 10. Figure 10: Illustration of the change of execution status between navigation, execution and synchronization (left), for a team Ck given its local plan Γk (right) in (17) and (19). Note that the concurrency constraint between tasks ω2, ω5 are enforced by the synchronization state (red line). coordination messages cannot propagate and effective collab￾oration cannot be realized. Moreover, latency, packet loss, and ban… view at source ↗
Figure 12
Figure 12. Figure 12: Simulated human-in-the-loop scenario of 80 robots in dynamic environment. Top: in total 32 tasks and 482 subtasks are performed during the mission time of 180.4 s, with the time elapsed from left to right. Clearly κ1 to κ4 are issued in the process; Bottom-left: the snapshots of execution of tasks at t “ 15 s, where different markers indicate different types of robots; Bottom-right: gantt charts of teams … view at source ↗
Figure 13
Figure 13. Figure 13: Final trajectories of local task executions for robots under the [PITH_FULL_IMAGE:figures/full_fig_p014_13.png] view at source ↗
Figure 15
Figure 15. Figure 15: Sankey diagram of all robots that participated in the overall mission, along with the number of teams and their compositions. Note that subteams are labeled N0 ´ N49 and robots are labeled A0 ´ A79. limited coalition changes. During this phase, the operator issues request κ4 for robot reassignment, transferring robot 21 from ω5 to support team N6 in completing subtasks 5, 9, 16 of ω3 under resource reallo… view at source ↗
Figure 16
Figure 16. Figure 16: Snapshots of baselines Flow and ScRATCHeS. Left: the subtasks in the red circle are unfinished after t “ 32s; Right: robots marked in red have long navigation distance due to inefficient assignments. C. Comparisons The proposed method is compared against eight baselines: (I) MILP, where a complete MILP is formulated for all robots N and tasks Ωt, similar to [13], [25], i.e., without the subteam formation;… view at source ↗
Figure 18
Figure 18. Figure 18: Parameter sensitivity and performance analysis of the HECTOR framework: parameter horizon H in assignment (top-left), redundancy mar￾gin α in team formation (top-right), the number of finished task in current horizon to trigger the replan (bottom-left), and the curvature κ of robots to plan the trajectories in task coordination (bottom-right). problem scale grows to 170 robots and 100 tasks. This effi￾cie… view at source ↗
Figure 20
Figure 20. Figure 20: High-fidelity ROS simulation of the proposed framework under the scene for disaster relief. Top-left: 2 UAVs assigned to static known tasks (green), 4 robots to dynamic known tasks (purple), and 6 robots to static unknown tasks (yellow); Middle: the task execution trajectories for 10 tasks over different regions and 36 subtasks. Bottom-right: the execution timeline of each robot completing assigned tasks … view at source ↗
read the original abstract

Robotic fleets can be extremely efficient when working concurrently and collaboratively, e.g., for delivery, surveillance, search and rescue. However, it can be demanding or even impractical for an operator to directly control each robot. Thus, autonomy of the fleet and its online interaction with the operator are both essential, particularly in dynamic and partially unknown environments. The operator might need to add new tasks, cancel some tasks, change priorities and modify planning results. How to design the procedure for these interactions and efficient algorithms to fulfill these needs have been mostly neglected in the related literature. Thus, this work proposes a human-centric coordination and supervision scheme (HECTOR) for large-scale robotic fleets under continual and uncertain temporal tasks. It consists of three hierarchical layers: (I) the bidirectional and multimodal protocol of online human-fleet interaction, where the operator interacts with and supervises the whole fleet; (II) the rolling assignment of currently-known tasks to teams within a certain horizon, and (III) the dynamic coordination within a team given the detected subtasks during online execution. The overall mission can be as general as temporal logic formulas over collaborative actions. Such hierarchical structure allows human interaction and supervision at different granularities and triggering conditions, to both improve computational efficiency and reduce human effort. Extensive human-in-the-loop simulations are performed over heterogeneous fleets under various temporal tasks and environmental uncertainties.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper proposes HECTOR, a three-layer human-centric hierarchical scheme for coordinating and supervising large-scale robotic fleets under continual and uncertain temporal tasks. Layer I provides a bidirectional multimodal protocol for online human-fleet interaction and supervision; Layer II performs rolling assignment of known tasks to teams within a planning horizon; Layer III handles dynamic intra-team coordination on detected subtasks. The hierarchy is claimed to enable supervision at varying granularities, thereby improving computational efficiency and reducing human effort, with support from human-in-the-loop simulations on heterogeneous fleets executing temporal tasks amid environmental uncertainties.

Significance. If the efficiency and human-effort claims are substantiated, the work would address a practical gap in scalable human oversight of robotic fleets in dynamic settings such as delivery, surveillance, and search-and-rescue. The multi-granularity interaction protocol and rolling-horizon decomposition could enable more deployable systems than flat or fully autonomous approaches, provided the computational tractability holds.

major comments (3)
  1. [Layer II description] Layer II (rolling assignment): no complexity analysis, solver type (MILP, heuristic, or otherwise), approximation guarantees, or empirical scaling of decision time versus fleet size N and task count M is provided. This is load-bearing for the central efficiency claim, because continual re-triggers from environmental uncertainties could eliminate any computational savings without such bounds or measurements.
  2. [Simulation results] Simulation evaluation: the human-in-the-loop results lack reported quantitative metrics (e.g., wall-clock time, number of human interventions, success rates), baselines, effect sizes for efficiency gains, or failure-case analysis under varying uncertainty levels. Without these, the claims of improved efficiency and reduced human effort cannot be verified.
  3. [Overall framework] Temporal-logic handling: the manuscript states that missions can be expressed as temporal logic formulas over collaborative actions, yet provides no details on how such formulas are decomposed or preserved across the three layers or any formal correctness argument for the hierarchical decomposition.
minor comments (2)
  1. [Abstract] The abstract would be strengthened by a single concrete example of a temporal task and the corresponding human intervention points.
  2. [Notation] Notation for teams, subtasks, and horizons should be introduced once and used consistently; currently the terms appear without explicit definitions in the high-level description.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed comments, which highlight important aspects needed to strengthen the efficiency and correctness claims of HECTOR. We address each major comment below and will incorporate revisions to improve the manuscript.

read point-by-point responses

  1. Referee: [Layer II description] Layer II (rolling assignment): no complexity analysis, solver type (MILP, heuristic, or otherwise), approximation guarantees, or empirical scaling of decision time versus fleet size N and task count M is provided. This is load-bearing for the central efficiency claim, because continual re-triggers from environmental uncertainties could eliminate any computational savings without such bounds or measurements.

    Authors: We agree that the absence of complexity analysis and scaling results for Layer II weakens the efficiency claims. The rolling assignment is formulated as a MILP and solved with a standard off-the-shelf solver; in the revised manuscript we will add a dedicated subsection providing worst-case complexity, a brief discussion of approximation guarantees for the rolling-horizon relaxation, and new empirical plots of decision time versus N and M under increasing uncertainty rates. These additions will directly address the concern that continual re-triggers could negate computational savings. revision: yes

  2. Referee: [Simulation results] Simulation evaluation: the human-in-the-loop results lack reported quantitative metrics (e.g., wall-clock time, number of human interventions, success rates), baselines, effect sizes for efficiency gains, or failure-case analysis under varying uncertainty levels. Without these, the claims of improved efficiency and reduced human effort cannot be verified.

    Authors: We acknowledge that the current simulation section reports only qualitative observations. In the revision we will augment the evaluation with quantitative tables and figures that include wall-clock times, counts of human interventions, task success rates, comparison against a flat centralized baseline and a fully autonomous variant, Cohen’s d effect sizes for efficiency gains, and a failure-case analysis across three uncertainty levels. These metrics will be obtained from the same human-in-the-loop setup already described. revision: yes

  3. Referee: [Overall framework] Temporal-logic handling: the manuscript states that missions can be expressed as temporal logic formulas over collaborative actions, yet provides no details on how such formulas are decomposed or preserved across the three layers or any formal correctness argument for the hierarchical decomposition.

    Authors: We agree that the manuscript currently lacks an explicit decomposition procedure and formal correctness argument. In the revised version we will insert a new subsection that (i) defines the syntax of collaborative temporal-logic formulas, (ii) describes the syntactic decomposition performed at each layer, and (iii) provides a sketch of the inductive proof that the hierarchical execution preserves the original formula semantics under the stated assumptions on subtask detection and team coordination. This will be supported by a small illustrative example. revision: yes

Circularity Check

0 steps flagged

No circularity: proposal is architectural with simulation support

full rationale

The paper presents HECTOR as a new three-layer hierarchical scheme for human-fleet interaction, rolling assignment, and dynamic coordination under temporal tasks. No equations, fitted parameters, or derivation steps appear in the abstract or description that reduce to self-definition, renamed inputs, or self-citation chains. Claims of efficiency and reduced human effort are framed as outcomes of the hierarchy and are supported by human-in-the-loop simulations rather than by construction from prior fitted results or uniqueness theorems. The structure is presented as addressing neglected interaction procedures, with no load-bearing self-referential definitions or ansatz smuggling.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central claim rests on the assumption that temporal logic can adequately capture collaborative fleet tasks and that the proposed layers yield efficiency gains, both introduced without independent external benchmarks in the abstract.

axioms (1)
  • domain assumption Temporal logic formulas over collaborative actions can represent general mission requirements for robotic fleets
    Invoked when stating that the overall mission can be as general as temporal logic formulas.
invented entities (1)
  • HECTOR hierarchical scheme no independent evidence
    purpose: Human-centric coordination and supervision of robotic fleets
    New framework proposed in this work with no independent evidence outside the described simulations.

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